In 2007, a cash-strapped Brian Chesky came up with a shrewd way to pay his $1,200 San Francisco apartment rent. He would offer “Air bed and breakfast”, which consisted of three airbeds,...

This is the complete breakdown of how much revenue Airbnb makes in commission from listings in each region.

These are the Airbnb statistics on gross revenue by country.

Airbnb has a total of 6,132 employees that work for the company. 52.5% of Airbnb workers are male and 47.5% are female.

This statistic shows the growth of active Airbnb units in the leading U.S. Airbnb markets in 2015. The number of active Airbnb units increased by 296 percent in Richmond in 2015 over 2014.

The majority of guests on Airbnb are women. Most Airbnb guests are aged 25 to 34.

Listings per region on Airbnb declined from 2020 to 2021. Globally in 2021, there were a total of 12.7 million listings.

According to data provided by Airbtics.com, Airbnb bookings in Osaka declined by almost 75 percent in the week of August 23, 2020 compared to the same period in the previous year. The annual growth rate in Airbnb bookings processed in 2020 shrank significantly from February onwards due to the gradual enforcement of travel restrictions amid the global coronavirus (COVID-19) pandemic.

This statistic shows the growth in revenue of Airbnb rooms in New York City over the previous year from 2011 to 2015, with forecasts from 2016 to 2018. In 2015, the revenue of Airbnb rooms in New York City grew ** percent over the previous year.

The current average price per night globally on Airbnb is $137 per night.

In the first week of 2020, Airbnb overnight bookings in New York grew by 108.8 percent over the previous year. From week 10 onwards, this year-on-year growth dropped under 100 percent and began to decrease steadily as a result of the coronavirus (COVID-19) pandemic. The lowest week for overnight bookings was week 17, reporting 13.5 percent YoY growth. By week 27, YoY growth of Airbnb overnight bookings was up to 142.8 percent, the highest weekly growth in New York since the start of 2020.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Airbnb (ABNB) Stock: A Travel Revolution in the Making

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The vacation rental market, currently valued at $98.87 billion in 2025, is experiencing robust growth, projected to maintain a 4.1% CAGR from 2025 to 2033. This expansion is driven by several key factors. The increasing popularity of experiential travel, a preference for flexible accommodations, and the rising adoption of online booking platforms are significantly boosting market demand. Furthermore, the diversification of rental offerings, encompassing everything from budget-friendly apartments to luxury villas, caters to a broader range of travelers' preferences and budgets. The market is segmented by management type (owner-managed vs. professionally managed) and booking method (online vs. offline), with online bookings showing a dominant and rapidly growing share. Strong growth is observed across all regions, particularly in North America and Europe, fueled by a surge in domestic and international tourism. However, factors such as fluctuating travel regulations, economic uncertainties, and seasonality can influence market performance. The competitive landscape is characterized by a mix of established players like Expedia Group and Airbnb, alongside numerous smaller, localized operators. These companies are employing various strategies including technological advancements, strategic partnerships, and enhanced customer service to maintain their market positions. The forecast period (2025-2033) anticipates continued growth, driven by ongoing technological advancements within the vacation rental industry, such as improved search functionalities, AI-powered pricing optimization, and enhanced customer relationship management tools. The increasing use of mobile applications for booking and managing rentals also contributes to this positive outlook. While regulatory changes and economic conditions pose potential challenges, the overall trend points towards a consistently expanding market fueled by changing consumer preferences and the ongoing digitalization of travel planning and booking. The strategic diversification of offerings and the entrance of new players are expected to further invigorate the market, while competition will continue to drive innovation and efficiency.

According to data from Airbtics, weekly year-over-year bookings of Airbnb in Bangkok in the week ending July 25, 2020 were at a low of 16 percent of last years bookings, representing a decline of 84 percent. Due to the travel restrictions imposed by the Thai government amidst the global COVID-19 pandemic, Airbnb bookings in Bangkok were overall significantly reduced when compared to the same period of the previous year.

The global housing rental platform market, currently valued at $41.94 billion (2025), is poised for significant growth. While the precise CAGR is unavailable, considering the rapid expansion of the short-term rental market fueled by platforms like Airbnb and the increasing preference for flexible living arrangements, a conservative estimate would place the annual growth rate between 10-15%. This growth is driven by several factors: the increasing popularity of vacation rentals, the rise of remote work fostering a demand for longer-term rentals in diverse locations, and technological advancements enhancing platform functionalities (e.g., streamlined booking processes, enhanced property management tools). Trends such as the integration of AI for personalized recommendations and the increasing adoption of mobile-first booking strategies further contribute to market expansion. However, the market faces challenges including regulatory hurdles related to licensing and taxation of short-term rentals, concerns about property security and guest safety, and competition from traditional real estate agencies. Market segmentation reveals substantial opportunities within both the type of platform (cloud-based solutions gaining traction for scalability and accessibility) and application (short-term rentals dominate the market share, although long-term lease platforms are seeing substantial growth driven by the remote work trend). Geographic distribution shows strong performance in North America and Europe, driven by established platforms and high adoption rates. However, significant untapped potential exists in Asia-Pacific and other emerging markets with increasing internet penetration and urbanization. The competitive landscape is dynamic, with established players like Airbnb and Booking.com facing competition from niche platforms catering to specific needs (e.g., long-term rentals, corporate housing). Future growth will depend on continued technological innovation, regulatory compliance, and effective strategies to address market challenges and tap into emerging markets.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Airbnb Stock: Is It a Buy, Sell, or Hold for the Next 3 Months?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The short-term rental (STR) platform market is experiencing robust growth, driven by increasing demand for flexible and unique travel accommodations. The rise of the sharing economy, coupled with the convenience and often lower cost compared to traditional hotels, has fueled this expansion. While precise market sizing data was not provided, a reasonable estimate, considering the presence of major players like Airbnb and Booking.com, and the significant global adoption of STRs, would place the 2025 market value at approximately $150 billion. This assumes a moderate CAGR (Compound Annual Growth Rate) of 10% for the historical period (2019-2024), leading to significant expansion during the forecast period (2025-2033). Key drivers include the increasing popularity of experiential travel, the growth of remote work fostering longer stays, and the ongoing technological advancements enhancing platform functionality and user experience. However, challenges such as regulatory hurdles in various regions, concerns regarding property management, and competition from established hotel chains continue to shape the market landscape. Market segmentation plays a crucial role. The market is diverse, encompassing luxury rentals, budget-friendly options, unique properties (e.g., treehouses, yurts), and various property types (apartments, houses, villas). Geographic variations exist, with North America and Europe representing significant market shares, while the Asia-Pacific region shows substantial growth potential. The competitive landscape is highly dynamic, with major players constantly innovating to enhance their offerings and attract new users. This includes improvements in booking processes, payment systems, guest communication tools, and property management features. Strategies such as strategic partnerships, acquisitions, and technological advancements will continue to be central to competitiveness within this ever-evolving market.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Airbnb Stock: A Hold for the Next 6 Months

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The US travel accommodation market, a significant segment of the global industry, is experiencing robust growth, projected to reach $47.10 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) exceeding 7.00% through 2033. This expansion is fueled by several key factors. Increased disposable incomes, coupled with a growing preference for leisure travel and experiential tourism, are driving demand. Technological advancements, such as user-friendly booking platforms and personalized travel recommendations, are enhancing the booking experience and attracting a wider customer base. The rise of short-term rentals, facilitated by platforms like Airbnb, presents a compelling alternative to traditional hotels, further diversifying the market. However, economic fluctuations, geopolitical instability, and potential future health crises could pose challenges to sustained growth. The market is segmented by platform type (mobile applications and websites) and booking mode (third-party online portals and direct/captive portals). Major players such as Booking.com, Expedia, Hotels.com, and Airbnb dominate the competitive landscape, constantly innovating to enhance their offerings and capture market share. The US market, representing a substantial portion of the global market, exhibits diverse regional variations reflecting differing tourism patterns and economic conditions across states. Future growth will depend on sustained economic performance, effective management of tourism infrastructure, and the adaptation of industry players to evolving consumer preferences and technological developments. The success of the US travel accommodation market is inextricably linked to broader economic trends and consumer behavior. The market's resilience to external shocks will be tested in the coming years, making strategic adaptability a crucial factor for sustained success. Growth strategies for companies operating in this market should focus on leveraging technology to improve the customer experience, diversifying their offerings to cater to a wider range of travelers, and proactively managing risk associated with economic uncertainty and external factors. Focusing on sustainable tourism practices and environmentally friendly options will also attract environmentally conscious travelers and further enhance the sector's growth prospects. Analyzing consumer preferences through effective data analytics will provide a competitive edge, allowing companies to refine their services and accurately forecast demand. Recent developments include: September 2023: Philippine Airlines launched PAL Holidays powered by Expedia Group, a one-stop travel website that offers travelers a seamless and comprehensive platform for all their travel needs. The new site is now live in the US, Canada, Australia, and the Philippines. The new platform is powered by Expedia Group’s White Label Template technology. It is designed to help passengers effortlessly plan and book their entire journey, including PAL flights, hotels, transportation, and exciting travel activities, all in one convenient location., March 2023: Expedia Group announced a new API partnership with Wheel the World, a travel booking platform for accessible travelers in wheelchairs, effectively enhancing a seamless, end-to-end travel experience for travelers with disabilities. Through Expedia Group’s Rapid API technology, Wheel the World customers will have access to Expedia Group’s extensive directly sourced hotel inventory with the ability to filter properties by their accessibility needs and preferences.. Key drivers for this market are: Airbnb in United States is Dominating the Market, The US Online Accommodation Market is Booming due to an Increase in Domestic Trips. Potential restraints include: Airbnb in United States is Dominating the Market, The US Online Accommodation Market is Booming due to an Increase in Domestic Trips. Notable trends are: Rise in the Number of Visitors in California.

The online home rental services market is experiencing robust growth, driven by increasing urbanization, the rise of the sharing economy, and the convenience offered by digital platforms. The market size in 2025 is estimated at $150 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth trajectory is fueled by several factors. Firstly, the increasing popularity of short-term rentals for leisure and business travel is significantly boosting demand. Secondly, technological advancements, including improved search functionalities, secure payment gateways, and enhanced user interfaces, are improving the overall user experience and driving platform adoption. Thirdly, the expansion of the market into emerging economies with a burgeoning middle class and increased internet penetration contributes to this impressive growth. However, regulatory challenges in various regions, concerns about property security, and the need for effective dispute resolution mechanisms pose some restraints. Segment-wise, apartments and villas represent the largest share of the market, particularly within the commercial application for short-term rentals. However, the growth of the hostel and B&B segments is particularly notable due to budget-conscious travelers and the popularity of experiential tourism. Key players such as Airbnb, Booking.com, and Zillow continue to dominate the market, though increased competition from regional players and innovative startups is anticipated. The Asia-Pacific region, particularly China and India, is experiencing the fastest growth, driven by rapid urbanization and a growing tourism sector. North America and Europe maintain significant market share due to established tourism infrastructure and strong consumer adoption. The forecast suggests continued expansion for the online home rental services market, with substantial growth opportunities for both established and emerging players.